SoroushVahidi/scidocs huggingface dataset:
WeChecker: efficient and precise detection of privilege escalation vulnerabilities in Android apps
(2015)

Due to the rapid increase of Android apps and their wide usage to handle personal data, a precise and large-scaling checker is in need to validate the apps' permission flow before they are listed on the market. Several tools have been proposed to detect sensitive data leaks in Android apps. But these tools are not applicable to large-scale analysis since they fail to deal with the arbitrary execution orders of different event handlers smartly. Event handlers are invoked by the framework based on the system state, therefore we cannot pre-determine their order of execution. Besides, since all exported components can be invoked by an external app, the execution orders of these components are also arbitrary. A naive way to simulate these two types of arbitrary execution orders yields a permutation of all event handlers in an app. The time complexity is O(n!) where n is the number of event handlers in an app. This leads to a high analysis overhead when n is big. To give an illustration, CHEX [10] found 50.73 entry points of 44 unique class types in an app on average. In this paper we propose an improved static taint analysis to deal with the challenge brought by the arbitrary execution orders without sacrificing the high precision. Our analysis does not need to make permutations and achieves a polynomial time complexity. We also propose to unify the array and map access with object reference by propagating access paths to reduce the number of false positives due to field-insensitivity and over approximation of array access and map access.
 We implement a tool, WeChecker, to detect privilege escalation vulnerabilities [7] in Android apps. WeChecker achieves 96% precision and 96% recall in the state-of-the-art test suite DriodBench (for compairson, the precision and recall of FlowDroid [1] are 86% and 93%, respectively). The evaluation of WeChecker on real apps shows that it is efficient (average analysis time of each app: 29.985s) and fits for large-scale checking.
Latent Fingerprint Matching: Fusion of Manually Marked and Derived Minutiae
(2010)

Matching unknown latent fingerprints lifted from crime scenes to full (rolled or plain) fingerprints in law enforcement databases is of critical importance for combating crime and fighting terrorism. Compared to good quality full fingerprints acquired using live-scan or inking methods during enrollment, latent fingerprints are often smudgy and blurred, capture only a small finger area, and have large nonlinear distortion. For this reason, features (minutiae and singular points) in latents are typically manually marked by trained latent examiners. However, this introduces an undesired interoperability problem between latent examiners and automatic fingerprint identification systems (AFIS); the features marked by examiners are not always compatible with those automatically extracted by AFIS, resulting in reduced matching accuracy. While the use of automatically extracted minutiae from latents can avoid interoperability problem, such minutiae tend to be very unreliable, because of the poor quality of latents. In this paper, we improve latent to full fingerprint matching accuracy by combining manually marked (ground truth) minutiae with automatically extracted minutiae. Experimental results on a public domain database, NIST SD27, demonstrate the effectiveness of the proposed algorithm.
A New Feature for Automatic Speaker Verification Anti-Spoofing: Constant Q Cepstral Coefficients
(2016)

Efforts to develop new countermeasures in order to protect automatic speaker verification from spoofing have intensified over recent years. The ASVspoof 2015 initiative showed that there is great potential to detect spoofing attacks, but also that the detection of previously unforeseen spoofing attacks remains challenging. This paper argues that there is more to be gained from the study of features rather than classifiers and introduces a new feature for spoofing detection based on the constant Q transform, a perceptually-inspired time-frequency analysis tool popular in the study of music. Experimental results obtained using the standard ASVspoof 2015 database show that, when coupled with a standard Gaussian mixture model-based classifier, the proposed constant Q cepstral coefficients (CQCCs) outperform all previously reported results by a significant margin. In particular, those for a subset of unknown spoofing attacks (for which no matched training data was used) is 0.46%, a relative improvement of 72% over the best, previously reported results.
Coco-Q: Learning in Stochastic Games with Side Payments
(2013)

Coco (“cooperative/competitive”) values are a solution concept for two-player normalform games with transferable utility, when binding agreements and side payments between players are possible. In this paper, we show that coco values can also be defined for stochastic games and can be learned using a simple variant of Q-learning that is provably convergent. We provide a set of examples showing how the strategies learned by the Coco-Q algorithm relate to those learned by existing multiagent Q-learning algorithms.
Deep Learning with Sets and Point Clouds
(2016)

We study a simple notion of structural invariance that readily suggests a parameter-sharing scheme in deep neural networks. In particular, we define structure as a collection of relations, and derive graph convolution and recurrent neural networks as special cases. We study composition of basic structures in defining models that are invariant to more complex “product” structures such as graph of graphs, sets of images or sequence of sets. For demonstration, our experimental results are focused on the setting where the discrete structure of interest is a set. We present results on several novel and non-trivial problems on sets, including point-cloud classification, set outlier detection and semi-supervised learning using clustering information.
Integration of Standardized and Non-Standardized Product Data
(2007)

Smart products adapt to environments, process contexts, users and other products. Standardized product data such as BMECat, eCl@ss, and EPC global formats are designed to support the exploitation of product data and therefore contribute to more efficient supply chains. Non-standardized product data mainly target soft benefits which targets the exploration of product data [Su04]. We present an extended product data model (SPDO) that integrates standardized and non-standardized product data. The SPDO model consists of five product information types that receive product data and, if available, associated explicit semantics. Explicit semantics are used for advanced services, such as compatibility testing or product bundling. Exemplified by the GS1 approach, we present how standardized product data is retrieved via RFID-based Electronic Product Codes (EPC) and stored in SPDO instantiations.
Cognitive radio network security: A survey
(2012)

Recent advancements in wireless communication are creating a spectrum shortage problem on a daily basis. Recently, Cognitive Radio (CR), a novel technology, has attempted to minimize this problem by dynamically using the free spectrum in wireless communications and mobile computing. Cognitive radio networks (CRNs) can be formed using cognitive radios by extending the radio link features to network layer functions. The objective of CRN architecture is to improve the whole network operation to fulfil the user’s demands anytime and anywhere, through accessing CRNs in a more efficient way, rather than by just linking spectral efficiency. CRNs are more flexible and exposed to wireless networks compared with other traditional radio networks. Hence, there are many security threats to CRNs, more so than other traditional radio environments. The unique characteristics of CRNs make security more challenging. Several crucial issues have not yet been investigated in the area of security for CRNs. A typical public key infrastructure (PKI) scheme which achieves secure routing and other purposes in typical ad hoc networks is not enough to guarantee the security of CRNs under limited communication and computation resources. However, there has been increasing research attention on security threats caused specifically by CR techniques and special characteristics of CR in CRNs. Therefore, in this research, a survey of CRNs and their architectures and security issues has been carried out in a broad
Mining Social Science Publications for Survey Variables
(2017)

Research in Social Science is usually based on survey data where individual research questions relate to observable concepts (variables). However, due to a lack of standards for data citations a reliable identification of the variables used is often difficult. In this paper, we present a work-in-progress study that seeks to provide a solution to the variable detection task based on supervised machine learning algorithms, using a linguistic analysis pipeline to extract a rich feature set, including terminological concepts and similarity metric scores. Further, we present preliminary results on a small dataset that has been specifically designed for this task, yielding a significant increase in performance over the random baseline.
Polygonal boundary approximation for a 2D general sweep based on envelope and boolean operations
(2000)

This paper presents an algorithm that approximates (using polygons) the boundary of a general sweep for an arbitrary 2D curved object (possibly with holes). Based on set-theoretic properties of the general sweep, our algorithm generates the polygonal sweep boundary incrementally, where envelope approximations and union operations are repeatedly applied to intermediate boundaries of the sweep and consecutive instances of the moving object at sampled locations of the motion. For approximation, each instance of the object is polygonized along the motion, where the object may experience dynamic shape transformation with topological changes such as creating and /or destroying internal holes. The incremental nature of the proposed algorithm makes the boundary construction of a general sweep useful for applications in interactive shape design, collision detection, and mechanical part design. Our algorithm generates a precise approximation of the boundary of a general sweep with real-time performance in computing unsweeps, Minkowski sums and differences, and constant radius offsets. Some experimental results are also given in this paper.
Multivariate lattices for encrypted image processing
(2015)

Images are inherently sensitive signals that require privacy-preserving solutions when processed in an untrusted environment, but their efficient encrypted processing is particularly challenging due to their structure and size. This work introduces a new cryptographic hard problem called m-RLWE (multivariate Ring Learning with Errors) extending RLWE. It gives support to lattice cryptosystems that allow for encrypted processing of multidimensional signals. We show an example cryptosystem and prove that it outperforms its RLWE counterpart in terms of security against basis-reduction attacks, efficiency and cipher expansion for encrypted image processing.
Epigenetics and depression: return of the repressed.
(2014)

INTRODUCTION
Epigenetics has recently emerged as a potential mechanism by which adverse environmental stimuli can result in persistent changes in gene expression. Epigenetic mechanisms function alongside the DNA sequence to modulate gene expression and ultimately influence protein production. The current review provides an introduction and overview of epigenetics with a particular focus on preclinical and clinical studies relevant to major depressive disorder (MDD).


METHODS
PubMed and Web of Science databases were interrogated from January 1995 up to December 2012 using combinations of search terms, including "epigenetic", "microRNA" and "DNA methylation" cross referenced with "depression", "early life stress" and "antidepressant".


RESULTS
There is an association between adverse environmental stimuli, such as early life stress, and epigenetic modification of gene expression. Epigenetic changes have been reported in humans with MDD and may serve as biomarkers to improve diagnosis. Antidepressant treatments appear to reverse or initiate compensatory epigenetic alterations that may be relevant to their mechanism of action.


LIMITATIONS
As a narrative review, the current report was interpretive and qualitative in nature.


CONCLUSION
Epigenetic modification of gene expression provides a mechanism for understanding the link between long-term effects of adverse life events and the changes in gene expression that are associated with depression. Although still a developing field, in the future, epigenetic modifications of gene expression may provide novel biomarkers to predict future susceptibility and/or onset of MDD, improve diagnosis, and aid in the development of epigenetics-based therapies for depression.
Assessing attractiveness in online dating profiles
(2008)

Online dating systems play a prominent role in the social lives of millions of their users, but little research has considered how users perceive one another through their personal profiles. We examined how users perceive attractiveness in online dating profiles, which provide their first exposure to a potential partner. Participants rated whole profiles and profile components on such qualities as how attractive, extraverted, and genuine and trustworthy they appeared. As past research in the psychology of attraction would suggest, the attractiveness and other qualities of the photograph were the strongest predictors of whole profile attractiveness, but they were not alone: the free-text component also played an important role in predicting overall attractiveness. In turn, numerous other qualities predicted the attractiveness ratings of photos and free-text components, albeit in different ways for men and women. The fixed-choice elements of a profile, however, were unrelated to attractiveness.
Survey of Authentication and Authorization for the Internet of Things
(2018)

The Internet of Things is currently getting significant interest from the scientific community. Academia and industry are both focused on moving ahead in attempts to enhance usability, maintainability, and security through standardization and development of best practices.We focus on security because of its impact as one of themost limiting factors to wider Internet ofThings adoption. Numerous research areas exist in the security domain, ranging from cryptography to network security to identitymanagement.This paper provides a survey of existing research applicable to the Internet of Things environment at the application layer in the areas of identity management, authentication, and authorization. We survey and analyze more than 200 articles, categorize them, and present current trends in the Internet of Things security domain.
Measuring the perceived quality of an AR-based learning application: a multidimensional model
(2017)

Augmented reality (AR) technologies could enhance learning in several ways. The quality of an AR-based educational platform is a combination of key features that manifests in usability, usefulness, and enjoyment for the learner. In this paper, we present a multidimensional model to measure the quality of an AR-based application as perceived by students. The model is specified by a second-order factor (perceived quality) and three dimensions: ergonomic quality, learning quality, and hedonic quality. The purpose of this model is to embody previous research into a coherent framework for the evaluation of AR-based educational platforms and to provide guidance for researchers and practitioners. The model was empirically validated on a Chemistry learning scenario and the results confirm the importance of both the learning and hedonic quality. ARTICLE HISTORY Received 23 July 2014 Revised 2 December 2015 Accepted 9 December 2015
Explainable Prediction of Medical Codes from Clinical Text
(2018)

Clinical notes are text documents that are created by clinicians for each patient encounter. They are typically accompanied by medical codes, which describe the diagnosis and treatment. Annotating these codes is labor intensive and error prone; furthermore, the connection between the codes and the text is not annotated, obscuring the reasons and details behind specific diagnoses and treatments. We present an attentional convolutional network that predicts medical codes from clinical text. Our method aggregates information across the document using a convolutional neural network, and uses an attention mechanism to select the most relevant segments for each of the thousands of possible codes. The method is accurate, achieving precision@8 of 0.71 and a Micro-F1 of 0.54, which are both better than the prior state of the art. Furthermore, through an interpretability evaluation by a physician, we show that the attention mechanism identifies meaningful explanations for each code assignment.
Content-based approach for Vietnamese spam SMS filtering
(2016)

Short Message Service (SMS) spam is a serious problem in Vietnam because of the availability of very cheap prepaid SMS packages. There are some systems to detect and filter spam messages for English, most of which use machine learning techniques to analyze the content of messages and classify them. For Vietnamese, there is some research on spam email filtering but none focused on SMS. In this work, we propose the first system for filtering Vietnamese spam SMS. We first propose an appropriate preprocessing method since existing tools for Vietnamese preprocessing cannot give good accuracy on our dataset. We then experiment with vector representations and classifiers to find the best model for this problem. Our system achieves an accuracy of 94% when labelling spam messages while the misclassification rate of legitimate messages is relatively small, about only 0.4%. This is an encouraging result compared to that of English and can be served as a strong baseline for future development of Vietnamese SMS spam prevention systems.
Raimond: Quantitative Data Extraction from Twitter to Describe Events
(2015)

Social media play a decisive role in communicating and spreading information during global events. In particular, real-time microblogging platforms such as Twitter have become prevalent. Researchers have used microblogging for a number of tasks, including past events analysis, predictions, and information retrieval. Nevertheless, little attention has been given to quantitative data extraction. In this paper, we address two questions: can we develop a mechanism to extract quantitative data from a collection of tweets, and can we use the salient findings to describe an event? To answer the first question, we introduce Raimond, a virtual text curator, specialized in quantitative data extraction from Twitter. To address the second question, we use our system on three events and evaluate its output using a crowdsourcing strategy. We demonstrate the e↵ectiveness of our approach with a number of real world examples.
A novel ultra low power temperature sensor for UHF RFID tag chip
(2007)

A novel ultra low power temperature sensor for UHF RFID tag chip is presented. The sensor consists of a constant pulse generator, a temperature related oscillator, a counter and a bias. Conversion of temperature to digital output is fulfilled by counting the number of the clocks of the temperature related oscillator in a constant pulse period. The sensor uses time domain comparing, where high power consumption bandgap voltage references and traditional ADCs arc not needed. The sensor is realized in a standard 0.18 mum CMOS process, and the area is only 0.2 mm2. The accuracy of the temperature sensor is plusmn1degC after calibration. The power consumption of the sensor is only 0.9 muW.
Benefits of an active spine supported bounding locomotion with a small compliant quadruped robot
(2013)

We studied the effect of the control of an active spine versus a fixed spine, on a quadruped robot running in bound gait. Active spine supported actuation led to faster locomotion, with less foot sliding on the ground, and a higher stability to go straight forward. However, we did no observe an improvement of cost of transport of the spine-actuated, faster robot system compared to the rigid spine.
Walking Robots and the Central and Peripheral Control of Locomotion in Insects
(1999)

This paper outlines aspects of locomotor control in insects that may serve as the basis for the design of controllers for autonomous hexapod robots. Control of insect walking can be considered hierarchical and modular. The brain determines onset, direction, and speed of walking. Coordination is done locally in the ganglia that control leg movements. Typically, networks of neurons capable of generating alternating contractions of antagonistic muscles (termed central pattern generators, or CPGs) control the stepping movements of individual legs. The legs are coordinated by interactions between the CPGs and sensory feedback from the moving legs. This peripheral feedback provides information about leg load, position, velocity, and acceleration, as well as information about joint angles and foot contact. In addition, both the central pattern generators and the sensory information that feeds them may be modulated or adjusted according to circumstances. Consequently, locomotion in insects is extraordinarily robust and adaptable.
"Cheap" Rapid Locomotion of a Quadruped Robot: Self-Stabilization of Bounding Gait
(2004)

The legged animals are capable of rapid, energy efficient, and adaptive locomotion in a complex environment. Toward a comprehensive understanding of the nature of such ecologically balanced legged locomotion, in this paper, we propose a novel method to achieve a form of bounding gait for a quadruped robot by using a minimalistic approach. Although this method uses a simple sinusoidal position control with no global sensory feedback, it is shown that the rapid bounding is possible in a relatively robust manner by properly exploiting the intrinsic dynamics and the interaction with the environment. The behavioral analyses with the robot experiments show that this relatively complicated dynamic locomotion is achieved even with a simple controller mainly because of a self-stabilization mechanism. Moreover, by exploiting this mechanism of self-stabilization, we propose a unique approach to control the forward velocity of the locomotion.
Modeling and Experiments of Untethered Quadrupedal Running with a Bounding Gait: The Scout II Robot
(2005)

In this paper we compare models and experiments involving Scout II, an untethered four-legged running robot with only one actuator per compliant leg. Scout II achieves dynamically stable running of up to 1.3 m s−1 on flat ground via a bounding gait. Energetics analysis reveals a highly efficient system with a specific resistance of only 1.4. The running controller requires no task-level or body-state feedback, and relies on the passive dynamics of the mechanical system. These results contribute to the increasing evidence that apparently complex dynamically dexterous tasks may be controlled via simple control laws. We discuss general modeling issues for dynamically stable legged robots. Two simulation models are compared with experimental data to test the validity of common simplifying assumptions. The need for including motor saturation and non-rigid torque transmission characteristics in simulation models is demonstrated. Similar issues are likely to be important in other dynamically stable legged robots as well. An extensive suite of experimental results documents the robot’s performance and the validity of the proposed